Preethi Maulik

Preethi Maulik is a senior product manager for GPU-accelerated decision optimization. Her technical background is in combinatorial optimization, mathematical programming, applied mathematics, and computer science, with end-to-end experience applying these methods to retail warehouse-management systems. Her work sits at the intersection of optimization algorithms, heuristic search, machine learning, and AI agents. She focuses on making optimization a core capability of agentic workflows through cuOpt skills and tool interfaces—enabling agents to move beyond retrieval, analysis, and prediction to formulate and solve constrained operational decisions. Preethi has worked across routing and scheduling, resource allocation, network planning, inventory optimization, and large-scale decision support in retail, energy, finance, telecommunications, and manufacturing. Her experience spans problem formulation, data and constraint modeling, algorithm and solver selection, enterprise integration, and production deployment. She is particularly focused on advancing the role of optimization in the AI-agent era while grounding solutions in the realities of imperfect data, operational constraints, and measurable performance trade-offs.